Multi-lesion radiomics model for discrimination of relapsing-remitting multiple sclerosis and neuropsychiatric systemic lupus erythematosus

• Published in: European radiology Vol. 32; no. 8; pp. 5700 - 5710
• Main Authors: Luo, Xiao, Piao, Sirong, Li, Haiqing, Li, Yuxin, Xia, Wei, Bao, Yifang, Liu, Xueling, Geng, Daoying, Wu, Hao, Yang, Liqin
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.08.2022; Springer Nature B.V
• Subjects: Algorithms; Autoimmune diseases; Brain; Chronic conditions; Diagnostic Radiology; Differential diagnosis; Feature extraction; Imaging; Imaging Informatics and Artificial Intelligence; Internal Medicine; Interventional Radiology; Lesions; Lupus; Machine learning; Medicine; Medicine & Public Health; Multiple sclerosis; Neurological complications; Neuroradiology; Radiology; Radiomics; Sensitivity; Systemic lupus erythematosus; Test sets; Training; Ultrasound; Support vector machine; Multiple sclerosis, relapsing-remitting; Lupus erythematosus, systemic; Image processing, computer-assisted; Machine learning
• ISSN: 1432-1084; 0938-7994; 1432-1084
• DOI: 10.1007/s00330-022-08653-2

Objectives To develop an MRI-based multi-lesion radiomics model for discrimination of relapsing-remitting multiple sclerosis (RRMS) and its mimicker neuropsychiatric systemic lupus erythematosus (NPSLE). Methods A total of 112 patients with RRMS ( n = 63) or NPSLE ( n = 49) were assigned to training and test sets with a ratio of 3:1. All lesions across the whole brain were manually segmented on T2-weighted fluid-attenuated inversion recovery images. For each single lesion, 371 radiomics features were extracted and trained using machine learning algorithms, producing Radiomics Index for Lesion (RIL) for each lesion and a single-lesion radiomics model. Then, for each subject, single lesions were assigned to one of two disease courts based on their distance to decision threshold, and a Radiomics Index for Subject (RIS) was calculated as the mean RIL value of lesions on the higher-weighted court. Accordingly, a subject-level discrimination model was constructed and compared with performances of two radiologists. Results The subject-based discrimination model satisfactorily differentiated RRMS and NPSLE in both training (AUC = 0.967, accuracy = 0.892, sensitivity = 0.917, and specificity = 0.872) and test sets (AUC = 0.962, accuracy = 0.931, sensitivity = 1.000, and specificity = 0.875), significantly better than the single-lesion radiomics method (training: p < 0.001; test: p = 0.001) Besides, the discrimination model significantly outperformed the senior radiologist in the training set (training: p = 0.018; test: p = 0.077) and the junior radiologist in both the training and test sets (training: p = 0.008; test: p = 0.023). Conclusions The multi-lesion radiomics model could effectively discriminate between RRMS and NPSLE, providing a supplementary tool for accurate differential diagnosis of the two diseases. Key Points • Radiomic features of brain lesions in RRMS and NPSLE were different. • The multi-lesion radiomics model constructed using a merging strategy was comprehensively superior to the single-lesion-based model for discrimination of RRMS and NPSLE. • The RRMS-NPSLE discrimination model showed a significantly better performance or a trend toward significance than the radiologists.